RTX 5060 Ti 16GB经济之选16 GB VRAM (+5)
C
Adds memory headroom for longer context windows and future model growth.42.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can solar finalised finetuned Model 10.7B i1 run on RTX 2080 Ti 11GB?
YES — Tight Fit
C52Usable
Estimated from fit model
solar finalised finetuned Model 10.7B i1 needs ~10.1 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~61 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 10.1 GB, 61.3 tok/s, Tight fit
10.1 GB required11.0 GB available
92% VRAM used
Fit status
Tight fit
Decode
61.3 tok/s
TTFT
3156 ms
Safe context
28K
Memory
10.1 GB / 11.0 GB
Memory breakdown
Weights6.5 GB
KV Cache1.3 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelssolar finalised finetuned Model 10.7B i1 on RTX 2080 Ti 11GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 61.3 tok/s decode · 3.2s TTFT (warm) · 153 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 61.3 tok/s | 1721 ms | 28K |
| Coding | C | Tight fit | 61.3 tok/s | 3156 ms | 28K |
| Agentic Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 41.9 tok/s | 6722 ms | 28K |
| Reasoning | C | Tight fit | 61.3 tok/s | 3730 ms | 28K |
| RAG | C | Runs with offload (needs ~0.2 GB host RAM) | 41.9 tok/s | 8402 ms | 28K |
Quantization options
How solar finalised finetuned Model 10.7B i1 (10.699999809265137B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.2 GB | Low | C52 |
Q3_K_S | 3 | 5.2 GB | Low | C52 |
NVFP4 | 4 | 6.0 GB | Medium | C52 |
Q4_K_M | 4 | 6.5 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 7.7 GB | High | C51 |
Q6_K | 6 | 8.8 GB | High | F0 |
Q8_0 | 8 | 11.4 GB | Very High | F0 |
F16 | 16 | 21.9 GB | Maximum | F0 |
Get started
Copy-paste commands to run solar finalised finetuned Model 10.7B i1 on your machine.
Run
lms load hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf && lms server start升级选项
能流畅运行 solar finalised finetuned Model 10.7B i1 的硬件
RTX 5060 Ti 16GB经济之选RTX 4060 Ti 16GB最佳性价比16 GB VRAM (+5)
C
Adds memory headroom for longer context windows and future model growth.32.2 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 5070 12GBNVIDIA 升级12 GB VRAM (+1)672 GB/s (+56)
C
This setup is broadly balanced for this model.64.9 tok/s 解码
~$549 MSRP
Frequently asked questions
Can RTX 2080 Ti 11GB run solar finalised finetuned Model 10.7B i1?
Yes, RTX 2080 Ti 11GB can run solar finalised finetuned Model 10.7B i1 with a C grade (Tight fit). Expected decode speed: 61.3 tok/s.
How much VRAM does solar finalised finetuned Model 10.7B i1 need?
solar finalised finetuned Model 10.7B i1 (10.699999809265137B parameters) requires approximately 10.1 GB of memory with Q4_K_M quantization.
What is the best quantization for solar finalised finetuned Model 10.7B i1?
The recommended quantization for solar finalised finetuned Model 10.7B i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will solar finalised finetuned Model 10.7B i1 run at on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, solar finalised finetuned Model 10.7B i1 achieves approximately 61.3 tokens per second decode speed with a time-to-first-token of 3156ms using Q4_K_M quantization.
Can RTX 2080 Ti 11GB run solar finalised finetuned Model 10.7B i1 for coding?
For coding workloads, solar finalised finetuned Model 10.7B i1 on RTX 2080 Ti 11GB receives a C grade with 61.3 tok/s and 28K context.
What context window can solar finalised finetuned Model 10.7B i1 use on RTX 2080 Ti 11GB?
On RTX 2080 Ti 11GB, solar finalised finetuned Model 10.7B i1 can safely use up to 28K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-mradermacher--solar-finalised-finetuned-model-10-7b-i1-gguf-on-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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